(My original concepts, language polished with the OpenAI o3 model)

When I first wrote about user‑owned “Brain Balls” in April (see the original post here: Has ChatGPT’s dominance already set the stage for the next big opportunity in AI?), the idea that we could carry our entire chat history from model to model still felt audacious. Just two months later the landscape has evolved again. Five signals now convince me that the next generation of agents will be more personal, modular, and privacy‑preserving than anything we have used so far.

1. Edge‑side multimodal models are taking over “branch” tasks #

Google’s preview of Gemma 3n shows how much horsepower now fits in a pocket‑sized device: int‑4‑quantised weights, latency under 40 ms, and native vision‑audio‑text fusion all running on mobile NPUs. In practical terms, your phone can now summarize a video meeting, identify a product in the camera viewfinder, or draft a reply — all without touching the cloud.

2. Flagship LLMs shift from doers to planners #

At WWDC 25 Apple introduced Apple Intelligence, a privacy‑first split architecture: the cloud plans, but sensitive inference runs locally. Regulated industries love this model; so will consumers once they see that private data never leaves their devices.

3. Memory is no longer a UX after‑thought #

Stateless chat logs won’t cut it when dozens of agents juggle calendars, finances, and health data. Open‑source projects such as Mem0 promote memory to a first‑class module: ranking, pruning, and surfacing just the right sliver of context in real time. Smart memory makes agents consistent, reduces hallucinations, and keeps retrieval costs sane.

4. Structure: the multi‑gene blueprint that drives an agent #

Think of Structure as a compact, executable blueprint that lives one layer above prompts.

• Executable behavior: Each Structure block can be parsed and run by the agent runtime, directly shaping decision logic rather than merely hinting at tone or style.
• Modular “gene fragments”: A single agent may carry several Structures—say, a negotiation module, a compliance module, and a creativity module—that switch in or out as context changes.
• Closed‑loop evolution: Because each fragment produces measurable outcomes, it can be verified, corrected, and upgraded, creating a feedback loop for continuous refinement.
• Extreme compression & encapsulation: Structures strive to express maximal strategy in minimal syntax, making them easy to version‑control, reuse, or share.
• Cross‑system compatibility: By abstracting above any one ontology, a Structure can interoperate with multiple knowledge graphs or cognitive frameworks, resisting vendor lock‑in and logical silos.

In short, if a model’s weights are its biology, then Structures are its genetic engineering kits—small, swappable code lets that keep the agent coherent while letting it evolve.

5. Protocols are blooming into a rainforest of agent interoperability #

Two complementary standards set the tone: MCP (Model Context Protocol) wires models to enterprise data silos, while A2A (Agent‑to‑Agent) lets agents barter services, pass tokens, and enforce permissions across clouds. These protocols also govern how Structures are exchanged and verified between agents. Add verifiable digital IDs and embedded payment rails, and you get a dense ecosystem where specialized agents cooperate like micro‑services once did for software.

Why this matters #

Together, these shifts turn the original “Brain Ball” vision from data liberation into a modular operating system for life and work. Your memories travel with you; your personal DNA governs every delegated action; and edge devices shoulder heavy lifting while cloud planners orchestrate the big picture. With modular Structures, that operating system can be patched and upgraded without touching the underlying weights.